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- 저자 : Zongyan Wang (검색결과 3 건)
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Jazz Characteristics of the Chinese Bowed String Instrument Erhu in Sanshui and Lanhuahua
Zongyan Wang 아시아음악학회 2016 Asian Musicology Vol.26 No.-
Compared with wind and brass, bowed string instruments tend to be featured less frequently in jazz, an observation equally applicable to the Chinese two-stringed fiddle, erhu. However, jazz compositions which utilize erhu may convey exceptional sound quality along with various Chinese characteristics. As a representative figure in Chinese jazz fusion, Kong Hongwei created the erhu and piano duet, Sanshui, in collaboration with the prominent erhu performer Song Fei. Lanhuahua, a famous Chinese folk song arranged by Kong’s student, Hu Xiaoyang, has been chosen as a comparison with Sanshui. Besides a literature review on jazz theory and instrumentation, the author has conducted interviews with Kong and Hu about their music, primary intentions, and musical expressions. In an almost reciprocal manner, Kong and Hu implement jazz characteristics by exploring the various aspects of erhu playing, such as improvisation, modern performance gesture, jazz rhythm, the treatment of blue notes, and in particular, its capability to imitate the intonation of the human voice, which is key to the promotion and popularization of jazz in China.
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Zongyan Hu,Shilong Wang,Chi Ma 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.2
Reducing manufacturing cost is the main goal of machine tool precision allocation under the condition of meeting precision design requirements. The previous studies generally took "ensuring machining errors completely within the allowable range of design precision" as constraint. Due to the strict constraint, the cost reduction effect is limited. This paper proposes a new method of precision allocation based on precision reliability. The probability that the machining errors are within the design precision allowable range is taken as the measurement index of precision reliability, and the optimization model constraint is relaxed to "that the machining errors are within the allowable range of design precision with predefined precision reliability", so as to obtain lower manufacturing cost under tolerable precision loss risk. The case study of a large-scale gear hobbing machine shows that this method can effectively reduce the manufacturing cost, and the precision allocation is more economical and reasonable.
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Feature extraction based on PSO-FC optimizing KPCA and wear fault identification of planetary gear
Yan He,Linzheng Ye,Xijing Zhu,Zongyan Wang 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.6
The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gear has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used for nonlinear feature extraction, but the blind setting of kernel function parameters greatly affects the performance of KPCA algorithm. For the optimization of kernel parameters, it is necessary to study theoretical modeling to improve KPCA performance. In this paper, employing a Fisher criterion (FC) discriminant function in pattern recognition, the optimization mathematical model of the kernel parameter was presented and the improved particle swarm optimization algorithm (PSO) was applied to search for the optimum value, and the performance of the Kernel principal component analysis for nonlinear problems was improved. The optimized KPCA was applied for feature extraction of different wear fault modes of a planetary gear, and the feature dimensions were reduced from 27 to 10. The feature parameters with 92.9 % contribution rates were retained and sample sets were formed to feed the support vector machine (SVM) for final classification and identification. The intelligently optimized KPCA based on the PSO-FC has improved the structural distribution of data in the feature space and showed a good scale clustering effect in planetary gear wear state recognition. The accuracy of the SVM classification was improved by 17.5 %.
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